A Novel Particle Swarm Optimization Algorithm for k-Coverage Problems in Wireless Sensor Networks

In wireless sensor network, since the sensor signal coverage is directly related to the optimization of sensor resources, it is one of the most basic problems. To find the minimum number of sensors needed for the deployment and optimize the positions of sensors, self-adaptive estimation particle swarm optimization (SEPSO) is adopted. In some scenes, sensors should be avoided being placed in the area where it is inconvenient to make deployment, and therefore in the process of searching for positions of sensors, a re-deployment method called supplementary boundary condition for SEPSO is proposed to deal with those errant sensors. Extensive experiment results showed that, compared with the virtual force approach, the proposed method can achieve better deployment with less computing time. When the number of sensors is small, the virtual force method performs well, but when the number of sensors increases gradually, the calculation time will increase significantly, but the result does not improve compared with PSO. Two application experiments were conducted in an office scene and in a forest park scene, and the results in both experiments validated the feasibility of the proposed method.